Wikipedia Analysis.

1. BeautifulSoup to scrape; tokenization + stopword removal.
2. Frequency counts → charts; simple Flask UI.

Architecture

Data flow

1. User Input User submits Wikipedia article URL through the web interface. URL validation ensures it's a proper Wikipedia domain to prevent scraping arbitrary websites.
2. Content Extraction BeautifulSoup fetches the page and parses the HTML, targeting the main content div (#mw-content-text) while excluding infoboxes, reference sections, and navigation elements to get pure article text.
3. Text Normalization Raw HTML is converted to plain text, stripped of special characters and numbers. Text is lowercased and split into individual tokens using whitespace and punctuation as delimiters.
4. Filtering & Counting Tokens are filtered against an expanded stopword list (common words like 'the', 'is', 'at' plus Wikipedia-specific terms). Remaining tokens are counted using a frequency distribution, with the top 20-30 terms selected for visualization.
5. Visualization Rendering Frequency data is passed to Chart.js which renders an interactive bar chart. The chart displays terms on the x-axis and occurrence counts on the y-axis, with colors indicating relative frequency intensity.

Key decisions and trade-offs

Code highlights

def scrape_wikipedia(url):
    response = requests.get(url)
    soup = BeautifulSoup(response.content, 'html.parser')

    # Extract main content, exclude references and nav
    content_div = soup.find('div', {'id': 'mw-content-text'})

    # Remove unwanted sections
    for unwanted in content_div.find_all(['table', 'sup', 'div'], class_=['infobox', 'reference', 'navbox']):
        unwanted.decompose()

    return content_div.get_text()

from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize

def process_text(text):
    # Tokenize and normalize
    tokens = word_tokenize(text.lower())

    # Extended stopword list
    stop_words = set(stopwords.words('english'))
    wiki_stops = {'wikipedia', 'cite', 'reference', 'retrieved', 'archive', 'edit', 'page'}
    all_stops = stop_words.union(wiki_stops)

    # Filter and count
    filtered = [t for t in tokens if t.isalpha() and t not in all_stops]
    freq_dist = FreqDist(filtered)

    return freq_dist.most_common(25)

@app.route('/analyze', methods=['POST'])
def analyze():
    url = request.json.get('url')

    if not is_valid_wikipedia_url(url):
        return jsonify({'error': 'Invalid Wikipedia URL'}), 400

    try:
        text = scrape_wikipedia(url)
        top_words = process_text(text)

        return jsonify({
            'success': True,
            'data': {
                'words': [w[0] for w in top_words],
                'frequencies': [w[1] for w in top_words]
            }
        })
    except Exception as e:
        return jsonify({'error': str(e)}), 500

• Minutes to insights; handy for research & notes.

• Generic NLP techniques require customization for specific domains. Wikipedia articles contain structural elements (citations, infoboxes, navigation) that must be filtered out. Building domain-aware stopword lists improved result quality dramatically, reducing noise from metadata terms that would otherwise dominate frequency counts.

• Web scraping is brittle. Wikipedia's HTML structure can change, breaking CSS selectors. The scraper needed defensive programming, checking if elements exist before accessing them, handling various page types (disambiguation, redirect, standard), and gracefully degrading when structure doesn't match expectations.

• Perfect linguistic analysis wasn't the goal, quick insights were. Chose simple tokenization over advanced NLP (stemming, lemmatization, named entity recognition) because speed and simplicity outweighed marginal accuracy gains. Sometimes 80% accuracy in milliseconds beats 95% accuracy in seconds.

• Raw frequency lists are hard to interpret. Visual charts transformed data into immediate understanding, users could glance at a bar chart and instantly grasp article themes. The visualization layer turned out to be as important as the analysis itself for delivering value.

• Processing individual Wikipedia articles works well, but the architecture doesn't scale to bulk analysis of hundreds of articles. If revisiting this project, I'd add caching, batch processing capabilities, and potentially move to Wikipedia dumps for large-scale corpus analysis rather than real-time scraping.